Chiral analytical method development of liquiritigenin with application to a pharmacokinetic study
Bibliographic record
Abstract
Pharmacometric characterization studies of liquiritigenin have historically overlooked its chiral nature. To achieve complete characterization, an analytical method enabling the detection and quantification of the individual enantiomers of racemic (±) liquiritigenin is necessary. Resolution of the enantiomers of liquiritigenin was achieved using a simple high-performance liquid chromatographic method. A Chiralpak® ADRH column was employed to perform baseline separation with UV detection at 210 nm.The standard curves were linear ranging from 0.5 to 100 µg/mL for each enantiomer. Limit of quantification was 0.5 µg/mL. The assay was applied successfully to stereoselective serum disposition of liquiritigenin enantiomers in rats. Liquiritigenin enantiomers were detected in serum as both aglycones and glucuronidated conjugates. Both unconjugated enantiomers had a serum half-life of ~15 min in rats. The volume of distribution (V(d) ) for S- and R-liquiritigenin was 1.49 and 2.21 L/kg, respectively. Total clearance (Cl(total) ) was 5.12 L/h/kg for S-liquiritigenin and 4.79 L/h/kg for R-liquiritigenin, and area under the curve (AUC(0-inf) ) was 3.95 µg h/mL for S-liquiritigenin and 4.23 µg h/mL for R-liquiritigenin. The large volume of distribution coupled with the short serum half-life suggests extensive distribution of liquiritigenin into tissues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".